Data Governance strategy
Data is a valuable commodity essential for strategic decision-making and reducing ROI from faster, more reliable intelligence. As the value of data increases, so does the need to secure and manage it to maximize its value, and to protect it within evolving regulatory and legislative controls. Implementing Data Governance strategy can ensure availability of reliable and accurate data to enable decision making. Data Governance is the driving force to centralize, standardize, and protect data and metadata consistently across an organization. Elait’s Data Governance Services encompass : Although these areas of Governance may seem unique, there is very much a dependency between them. Organisations will have contrasting reasons for needing Data Governance Strategies. They may adopt different approaches or utilize varying software to achieve this. However, implementing a straightforward and robust process is essential for a successful Data Governance program, preferably via a programmatic approach for a cost-effective, efficient and reliable solution. Our enterprise data governance solutions increase understanding, security and trust around an organization’s data among its stakeholders, this is vital, especially as companies scale and accumulate more data sources and assets.
data goverence
Enterprise Data Governance

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Master And Reference Data Management

Master and Reference data management

In conjunction with a data analytics strategy, an effective Master Data Management service facilitates running, growing, and transforming the business. It ensures that master data is clean, consistent, and reliable. This, in turn, enables accurate reporting, faster and more reliable decision making; thereby improving business operations, lessening compliance risk, reducing time to market, and identifying new channels, markets and opportunities.

Business Challenges


Inconsistent master data management services within the organisation prevent consistent analysis, resulting in conflicting or spurious interpretations. This, in turn, damages confidence, leads to suboptimal decision making, and prevents accurate and agile forecasting.

The lack of strategy and ownership proliferates cottage industries and inflates costs.

Our Offering

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Establishing a centralised source and storage of Master and Reference Data using industry best practices.

Master and Reference data

Establish processes to assign ownership of Master and Reference data.


Align technology with the corporate architectural approach but adopt a business-led approach (rather than a technology-centric project) to ensure scope and cost are driven by business value.
For sustainable operational governance proactively engage with business stakeholders and collaborate between business and IT to establish policies, procedures, and measures for success (KPIs), preferably utilising a programmatic approach that leverages frameworks.
Centralise key information and progressively adopt this across the organisation to establish a trusted and reliable single version of the truth.

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Data Quality And Validation

Data Quality issues exacerbate the problems described in Master Data Management Services and contribute to information distrust, degrade business value and negatively impact financial performance and customer experiences. The only way to build confidence in enterprise data is to consistently measure, correct, and monitor data quality across the enterprise within a sustainable program that focuses on business value, correcting data at the optimal location.

data quality

Business Challenges


Loss of revenue is experienced due to diminished competitive advantage, missed opportunities, inaccurate marketing, and communications.


Reputational damage is incurred if trading with customers based on insufficient or incorrect information. Punitive fines may be levied if data protection or industry-specific regulations are inadvertently broken.

Automated Test Data Generation

Productivity is impacted as staff waste time on validating and fixing errors rather than focusing on their core mission. Cottage industries may spring up to cope with data quality issues.

Automated Test Data Generation

Productivity is impacted as staff waste time on validating and fixing errors rather than focusing on their core mission. Cottage industries may spring up to cope with data quality issues.

Our Offering


Data Quality Assessment to assess Data Quality metrics, track changes over time, and set about a program to improve quality through a data’s lifecycle.


Establish data quality improvements related to financial cost and business goals, addressing financial and operational performance, legal and regulatory compliance, and customer experience.
Engage with stakeholders to articulate impacts, priorities, financials, and performance measures.
Identify the current state of data quality and its business implications. Implement automated data profiling on an ongoing basis to accommodate new sources and data decay. Execute data correction initiatives to target corrections at the optimal location for longevity.
Establish Data Stewardship roles to take ownership of data and work with the business to manage the program’s objectives; converting policies into practice, advocating standards and best practices, ensuring data regulations are implemented, overseeing security, risk management, and data quality measures.

Data Lineage

Data Lineage

What is Data Lineage and why is it needed? 

Data Lineage is the ability to identify the source to target data movement and any associated rules against this data. It allows the business to do an Impact Analysis of changes within the data stream for new projects as well as legacy workstreams. Data Lineage also allows the business to meet the ever-increasing regulatory requirements to understand where your data exists and what has been done to the data throughout its journey.

Data Lineage-Technical and Business Flow


Business Challenges

Organisations lack the understanding of how and where data originates, this makes informed business decisions difficult.
Projects not having insight into data correctly cannot meet regulatory compliance.
Project initiation cannot make informed decisions on the impact of data stream changes leading to project timelines being extended and costs increasing.

Our Offering


Creating business glossary & publication.

logical thinking

Establish business, logic, & metadata mapping.


Define process for extracting Metadata from enterprise-wide data sources.


Map Technical Lineage for all source and target systems to allow business decisions to be made before a project starts instead of waiting for the project to begin to see the impacts on current and other projects.
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Map Business Lineage to Technical Lineage to give the business a more technical move of data and give a better understanding of the relationships.
Allow business users the ability to graphically represent data and data pipelines to meet regulatory compliance.

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Metadata Management

Metadata Management involves administering the data that describes the actual data. It is responsible for business definitions, reference data, ensuring data quality, consistency, and accuracy of data across various reporting systems. It centralises the information about data across the organisation, bringing together data from siloed Business Units. There are many different scenarios that you may, or may not, recognise. Each of these scenarios brings its unique challenges but also allows an organisation to become more robust when delivering future projects.


Business Challenges

Loss of the ability to control the content and ownership of critical data assets.
Loss of the ability to classify HIPPA, GDPR, and other data to meet regulatory requirements possibly incurring financial and reputational penalties.

Loss of the ability to classify HIPPA (Health Insurance Portability and Accountability Act), GDPR, and other data to meet regulatory requirements possibly incurring financial and reputational penalties.

Our Offering


Build a robust program to allow for
end-to-end usage of Metadata Management.


Build a robust Metadata Management solution that centralises the management of data. This in turn will allow previously siloed business units access to sources of data they may not have known about previously.
Building a robust solution will also build trust within the organisation to allow synergy between business units.

Data Security, Storage And Operations


Data security is the practice of protecting digital information from unauthorised access, corruption, or theft throughout its entire lifecycle. This begins with the organisation’s policies and procedures and permeates to physical access, hardware, software, data storage, administration activities, and data usage. 

Businesses are under increasing threat from cyber-criminal activities, but also need to be protected from insider threats, human error, and software defects (both internal and external). The risks of data security issues will only intensify due to the growing value of digital information, the ever-increasing sophistication of cyber-criminals, and ongoing introductions of regulatory and industry compliance rules and legislation.

Business Challenges

There is the risk of reputational damage from inadvertent data breaches.
There is the risk of businesses being exposed to ransomware or fraud from cybercrime.
There is the risk of punitive fines from compromising Data Protection or industry-specific regulations.

Our Offering

data security

Data security advisory to assist in creating data security and privacy architecture.


Data discovery & classification.


Data obfuscation & encryption.


Cloud Data Security.


Establish best practices to identify when, where and how long data will be stored.


Help the business to meet data retention, storage, and encryption requirements based on organisation-specific requirements.



Work with the Business and IT to implement or align with a Data Governance strategy that implements strategic organisation policies and procedures across the enterprise ensuring longevity via frameworks, extensibility, and education.

Review the current state and prioritise remediation according to risk and value.
Implement best practices including access and privilege management strategies, encryption (at rest, in transit), data masking, obfuscation, version history, backups, archiving, high availability, business continuity, virus and malware protection, asset usage, network access, monitoring and alerting, password policies, data retention, data usage, cryptographic controls, software and licensing, secure development practices.

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Data Stewardship And Data Ownership

Data Ownership is usually the owner of the data and is identified for purposes of being a single point of contact for the identified data. This person is usually of a higher-level position and does not deal with the details of the data. Data Stewardship is someone who would identify the rules that are associated with the data and enforce them. Together, they are responsible for enterprise-wide data quality.

A skilfully designed and deployed data stewardship program is vital to ensure high-quality data gets presented for the data analytics program.


Business Challenges


Lack of clear and precise Data Ownership and Data Stewardship through accountable parties.


Inadequate guidance on roles of the Data Stewards.

Our Offering


Develop policies, practices, and standards for data classification and management.

Master and Reference data

Establish processes to profile, analyse, define, standardise, cleanse, and monitor enterprise-wide data to ensure data assets are rightly managed for analytics.

data formats

Define data formats, resolve integration issues and ensure regulatory adherence.


Improved data quality by having Data Stewards actively involved leads to better data documentation and clear & concise data policies and processes.
Improved compliance with data-related regulations.
Periodic reports on data quality with clarity on lineage.
database server-img-data
Cleanse data systematically.
Improve productivity by reducing errors.

Create An Organisational Structure for Successful Data Governance?

Key Differentiators


Technology Competence


Advanced Data Integration


Resource Competence

Case Studies

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Data Governance

Using Data Quality Metrics To Drive Data Governance In A Financial Company

The corporation contacted Elait to help them identify Data Quality issues and build metrics to allow them to track and increase the quality of source data.

Data Governance

Creating Data Lineage To Meet Regulatory Requirements And Implement Data Governance

A major Financial Institution had a requirement to meet IFRS9 regulatory requirements to identify all data and transformations to data utilised for auditing purposes.

Trusted By


"We would recommend Elait to every company seeking a committed Data Warehouse Automation service provider. Elait has become our long-term technology partner and an integral part of our organisation. Elait has an extremely professional and dedicated staff with the willingness to undertake complex tasks to ensure project success".

- CTO of Insurance Company

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With Elait's Data Governance Services